Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms

We propose a routine for combining partial least squares-structural equation modeling (PLS-SEM) with selected machine learning (ML) algorithms to exploit the two method’s causal-predictive and causal-exploratory capabilities. Triangulating these two methods can improve the predictive accuracy of res...

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Bibliographic Details
Published in:Journal of business research Vol. 173; p. 114453
Main Authors: Richter, Nicole Franziska, Tudoran, Ana Alina
Format: Journal Article
Language:English
Published: Elsevier Inc 01.02.2024
Subjects:
Machine learning (ML)
Method triangulation
Partial least squares-structural equation modeling (PLS-SEM)
Prediction
Unified theory of acceptance and use of technology (UTAUT)
Machine learning (ML)
Partial least squares-structural equation modeling (PLS-SEM)
Method triangulation
Prediction
Unified theory of acceptance and use of technology (UTAUT)
ISSN:0148-2963, 1873-7978
Online Access:Get full text
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Summary:We propose a routine for combining partial least squares-structural equation modeling (PLS-SEM) with selected machine learning (ML) algorithms to exploit the two method’s causal-predictive and causal-exploratory capabilities. Triangulating these two methods can improve the predictive accuracy of research models, enhance the understanding of relationships, assist in identifying new relationships and therewith contribute to theorizing. We demonstrate the advantages and challenges of triangulating the two methods on an illustrative example along a four-step-routine: (1) Develop a PLS-SEM on a baseline conceptual model and use its standards to assess measurement model quality and generate latent variables scores. (2) Apply specific ML algorithms on the extracted data to validate relationships and identify new (linear) relationships that may go beyond the initial hypotheses; similarly, assess model advancements in the form of nonlinearities and interaction effects. (3) Evaluate the theoretical plausibility of alternative models. (4) Integrate alternative models in PLS-SEM and compare these with the baseline model using a recently proposed prediction-oriented test procedure in PLS-SEM.
ISSN:0148-2963
1873-7978
DOI:10.1016/j.jbusres.2023.114453